Our motivation in this study is the critical industry of Oil and Gas (O&G) that is aimed at becoming clean and ultimately unmanned, thereby safe, in Industry 4.0. O&G is one of the main environmental pollutants and even minor improvements in this industry can have major impacts in the global scale. In this context, there are several time-sensitive operations (e.g., fire detection [1]} and toxic gas monitoring [2]}) that failing to timely process them can potentially lead to a disasters, scuh as oil spill, explosion, and even death. Understanding the uncertainties exist in execution time of different application types and properly modeling them is crucial in architecting software solutions that are robust against these uncertainties. Note that DNN-based applications encompass both the training and inference stages [3]}. While the training stage is generally carried out offline, our focus in this study is on modeling the inference execution time that has to be accurately estimated for latency-sensitive and mission critical applications [4]}. For instance, accurate estimation of inference time is instrumental in calculating the completion time of arriving tasks that can, in turn, help to make more precise resource allocation decisions [5]}.
